A computing device quantifies an expected benefit from a calibrated coefficient of variation (CV) and/or a calibrated service level (SL). The target optimization model determines a number and a time a new requisition is placed for an item at each node of the plurality of nodes. A validation time value is updated using an incremental time value and the process is repeated until the validation time value is greater than or equal to a stop time.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to: automatically select a calibrated parameter value of a first parameter; receive an indicator of a validation horizon time period, wherein the validation horizon time period includes a start time, a stop time, and an incremental time; automatically initialize a validation time value based on the start time; automatically read requisition history data from a requisition history dataset, wherein the requisition history data includes previous requisitions placed during a time period prior to the start time for an item in a network that includes a plurality of nodes; (a) automatically generate demand data for each node of the plurality of nodes using a forecast model with the requisition history data and the selected calibrated parameter value, wherein the forecast model is configured to forecast a demand associated with the item at each node of the plurality of nodes; (b) automatically update the requisition history data and compute a simulated key performance indicator (KPI) value of a KPI by executing a target optimization model with the generated demand data, wherein the target optimization model is configured to determine a number and a time a new requisition is placed for the item at each node of the plurality of nodes; (c) automatically store the computed, simulated KPI value in association with the selected initial validation time value; (d) automatically update the initialized validation time value using the incremental time; automatically repeat (a)-(d) until the updated, initialized validation time value is greater than or equal to the stop time; automatically compute an aggregated KPI value as a sum of the stored KPI values for each node at each value of the initialized validation time value; output a comparison between the computed, aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the validation horizon time period; and automatically optimize a stockpile of the item in the network for each node of the plurality of nodes using the selected calibrated parameter value.
A computer system optimizes item stockpiles across a network of locations. It selects a "calibrated parameter," representing service level or coefficient of variation, then simulates item demand at each location using historical requisition data and a forecasting model that incorporates the calibrated parameter. A target optimization model then determines when and how many new requisitions to place for each item at each location, generating simulated KPI values like fill rate. These KPIs are stored, aggregated across all locations, and compared to historical KPIs. Finally, the system adjusts the item stockpile at each location based on the calibrated parameter to improve overall network performance. This process repeats over a validation time period defined by a start, stop and incremental time to validate results.
2. The non-transitory computer-readable medium of claim 1 , wherein the KPI is at least one of an on-hand disbursement value, a backlog value, a ready rate value, a fill rate value, and a back order ratio value.
The computer system described previously, where a computer system optimizes item stockpiles across a network of locations. It selects a "calibrated parameter," representing service level or coefficient of variation, then simulates item demand at each location using historical requisition data and a forecasting model that incorporates the calibrated parameter. A target optimization model then determines when and how many new requisitions to place for each item at each location, generating simulated KPI values like fill rate. These KPIs are stored, aggregated across all locations, and compared to historical KPIs. Finally, the system adjusts the item stockpile at each location based on the calibrated parameter to improve overall network performance. This process repeats over a validation time period defined by a start, stop and incremental time to validate results. The key performance indicator (KPI) used in the simulation and optimization process is selected from on-hand disbursement, backlog, ready rate, fill rate, or backorder ratio.
3. The non-transitory computer-readable medium of claim 1 , wherein the first parameter is at least one of a service level and a coefficient of variation.
The computer system described previously, where a computer system optimizes item stockpiles across a network of locations. It selects a "calibrated parameter," representing service level or coefficient of variation, then simulates item demand at each location using historical requisition data and a forecasting model that incorporates the calibrated parameter. A target optimization model then determines when and how many new requisitions to place for each item at each location, generating simulated KPI values like fill rate. These KPIs are stored, aggregated across all locations, and compared to historical KPIs. Finally, the system adjusts the item stockpile at each location based on the calibrated parameter to improve overall network performance. This process repeats over a validation time period defined by a start, stop and incremental time to validate results. The “calibrated parameter” adjusted by the system can be either a service level target or a coefficient of variation.
4. The non-transitory computer-readable medium of claim 1 , wherein the computer-readable instructions further cause the computing device to automatically output a comparison between the computed, aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the validation horizon time period to verify that the selected calibrated parameter value is configured correctly.
The computer system described previously, where a computer system optimizes item stockpiles across a network of locations. It selects a "calibrated parameter," representing service level or coefficient of variation, then simulates item demand at each location using historical requisition data and a forecasting model that incorporates the calibrated parameter. A target optimization model then determines when and how many new requisitions to place for each item at each location, generating simulated KPI values like fill rate. These KPIs are stored, aggregated across all locations, and compared to historical KPIs. Finally, the system adjusts the item stockpile at each location based on the calibrated parameter to improve overall network performance. This process repeats over a validation time period defined by a start, stop and incremental time to validate results. The system outputs a comparison between simulated and actual historical KPI data to verify the calibrated parameter is correctly configured.
5. The non-transitory computer-readable medium of claim 1 , wherein selecting the calibrated parameter value of the first parameter comprises computer-readable instructions that further cause the computing device to: receive indicators of a start value, a stop value, and an incremental value of the first parameter; receive an indicator of a calibration horizon time period, wherein the calibration horizon time period includes a calibration start time, a calibration stop time, and a calibration incremental time; automatically initialize a calibration time value based on the calibration start time; automatically select a test value for the first parameter as the start value; automatically read second requisition history data from the requisition history dataset, wherein the second requisition history data includes previous requisitions placed during the time period prior to the calibration start time for the item in the network that includes the plurality of nodes; (e) automatically generate second demand data for each node of the plurality of nodes using the forecast model with the second requisition history data and the selected test value; (f) automatically update the second requisition history data and compute a second simulated KPI value of the KPI by executing the target optimization model with the generated second demand data; (g) automatically store the computed, second simulated KPI value in association with the selected test value and the initialized calibration time value (h) automatically update the initialized calibration time value using the calibration incremental time; and (i) automatically repeat (e)-(h) until the updated, initialized calibration time value is greater than or equal to the calibration stop time.
To automatically determine the best value for the calibrated parameter (service level or coefficient of variation), the computer system first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This entire calibration process is repeated until the calibration time reaches its stop value, after which the optimal calibrated parameter value is selected based on the simulation results.
6. The non-transitory computer-readable medium of claim 5 , wherein after (i), selecting the calibrated parameter value of the first parameter further comprises computer-readable instructions that further cause the computing device to: (j) automatically compute a second aggregated KPI value as a sum of the stored second KPI values for each node at each value of the initialized calibration time value; (k) automatically update the selected test value using the incremental value; (l) automatically repeat (e)-(k) until the updated, selected test value is greater than or equal to the stop value; and automatically execute a stockpile parameter optimization model with the computed, second aggregated KPI values and the computed second aggregated KPI value; wherein the calibrated parameter value of the first parameter is selected based on results of execution of the stockpile parameter optimization model that ranks each value of the selected test value.
Building on the previous calibration process, the computer system now selects the optimal calibrated parameter. After simulating and storing KPI values for various test values of the parameter across a calibration time period, the system computes an aggregated KPI value for each test value. It then uses a stockpile parameter optimization model, which ranks each tested parameter value, to minimize backorders or maximize service levels, subject to a budget. The highest-ranked test value is then selected as the final calibrated parameter.
7. The non-transitory computer-readable medium of claim 5 , wherein the computer-readable instructions further cause the computing device to automatically output the selected calibrated parameter value.
The computer system as described in the calibration process, where To automatically determine the best value for the calibrated parameter (service level or coefficient of variation), the computer system first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This entire calibration process is repeated until the calibration time reaches its stop value, after which the optimal calibrated parameter value is selected based on the simulation results. Finally, the system outputs the selected, optimized calibrated parameter value.
8. The non-transitory computer-readable medium of claim 5 , wherein the computer-readable instructions further cause the computing device to automatically output a comparison between the computed, second aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the calibration horizon time period.
The computer system as described in the calibration process, where To automatically determine the best value for the calibrated parameter (service level or coefficient of variation), the computer system first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This entire calibration process is repeated until the calibration time reaches its stop value, after which the optimal calibrated parameter value is selected based on the simulation results. The system outputs a comparison between the simulated, aggregated KPI values and historical KPI data to show how well the selected parameters perform.
9. The non-transitory computer-readable medium of claim 6 , wherein the stockpile parameter optimization model minimizes a total network backorder while satisfying a budget constraint defined by data read from a stockpile budget dataset.
In the stockpile parameter optimization model used to select the calibrated parameter, as described previously, the goal is to minimize the total number of backorders across the entire network while staying within a pre-defined budget read from a stockpile budget dataset.
10. The non-transitory computer-readable medium of claim 6 , wherein the stockpile parameter optimization model maximizes a total network service level while satisfying a budget constraint defined by data read from a stockpile budget dataset.
In the stockpile parameter optimization model used to select the calibrated parameter, as described previously, the goal is to maximize the overall service level across the entire network while staying within a pre-defined budget read from a stockpile budget dataset.
11. The non-transitory computer-readable medium of claim 1 , wherein the automatically selecting the calibrated parameter value of the first parameter and the automatically computing the aggregated KPI value are performed periodically.
The computer system, where a computer system optimizes item stockpiles across a network of locations. It selects a "calibrated parameter," representing service level or coefficient of variation, then simulates item demand at each location using historical requisition data and a forecasting model that incorporates the calibrated parameter. A target optimization model then determines when and how many new requisitions to place for each item at each location, generating simulated KPI values like fill rate. These KPIs are stored, aggregated across all locations, and compared to historical KPIs. Finally, the system adjusts the item stockpile at each location based on the calibrated parameter to improve overall network performance. This process repeats over a validation time period defined by a start, stop and incremental time to validate results. The process of selecting the calibrated parameter and computing the aggregated KPI values is performed automatically and on a recurring basis.
12. A computing device comprising: a processor; and a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to automatically select a calibrated parameter value of a first parameter; receive an indicator of a validation horizon time period, wherein the validation horizon time period includes a start time, a stop time, and an incremental time; automatically initialize a validation time value based on the start time; automatically read requisition history data from a requisition history dataset, wherein the requisition history data includes previous requisitions placed during a time period prior to the start time for an item in a network that includes a plurality of nodes; (a) automatically generate demand data for each node of the plurality of nodes using a forecast model with the requisition history data and the selected calibrated parameter value, wherein the forecast model is configured to forecast a demand associated with the item at each node of the plurality of nodes; (b) automatically update the requisition history data and compute a simulated key performance indicator (KPI) value of a KPI by executing a target optimization model with the generated demand data, wherein the target optimization model is configured to determine a number and a time a new requisition is placed for the item at each node of the plurality of nodes; (c) automatically store the computed, simulated KPI value in association with the selected initial validation time value; (d) automatically update the initialized validation time value using the incremental time; automatically repeat (a)-(d) until the updated, initialized validation time value is greater than or equal to the stop time; automatically compute an aggregated KPI value as a sum of the stored KPI values for each node at each value of the initialized validation time value; output a comparison between the computed, aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the validation horizon time period; and automatically optimize a stockpile of the item in the network for each node of the plurality of nodes using the selected calibrated parameter value and the computed aggregated KPI value.
A computing device optimizes item stockpiles across a network of locations. It selects a calibrated parameter (service level or coefficient of variation), simulates item demand at each location using historical data and a forecasting model incorporating the parameter. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored, aggregated, and compared to historical KPIs. The system adjusts the item stockpile at each location based on the calibrated parameter to improve performance. The process repeats over a validation time period (start, stop, increment).
13. The computing device of claim 12 , wherein the first parameter is at least one of a service level and a coefficient of variation.
The computing device described previously, where a computing device optimizes item stockpiles across a network of locations. It selects a calibrated parameter (service level or coefficient of variation), simulates item demand at each location using historical data and a forecasting model incorporating the parameter. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored, aggregated, and compared to historical KPIs. The system adjusts the item stockpile at each location based on the calibrated parameter to improve performance. The process repeats over a validation time period (start, stop, increment). The calibrated parameter adjusted by the computing device can be either service level or coefficient of variation.
14. The computing device of claim 12 , wherein the computer-readable instructions further cause the computing device to automatically output a comparison between the computed, aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the validation horizon time period to verify that the selected calibrated parameter value is configured correctly.
The computing device described previously, where a computing device optimizes item stockpiles across a network of locations. It selects a calibrated parameter (service level or coefficient of variation), simulates item demand at each location using historical data and a forecasting model incorporating the parameter. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored, aggregated, and compared to historical KPIs. The system adjusts the item stockpile at each location based on the calibrated parameter to improve performance. The process repeats over a validation time period (start, stop, increment). The device outputs a comparison between simulated and actual historical KPI data to verify the calibrated parameter is correctly configured.
15. The computing device of claim 12 , wherein selecting the calibrated parameter value of the first parameter comprises computer-readable instructions that further cause the computing device to: receive indicators of a start value, a stop value, and an incremental value of the first parameter; receive an indicator of a calibration horizon time period, wherein the calibration horizon time period includes a calibration start time, a calibration stop time, and a calibration incremental time; automatically initialize a calibration time value based on the calibration start time; automatically select a test value for the first parameter as the start value; automatically read second requisition history data from the requisition history dataset, wherein the second requisition history data includes previous requisitions placed during the time period prior to the calibration start time for the item in the network that includes the plurality of nodes; (e) automatically generate second demand data for each node of the plurality of nodes using the forecast model with the second requisition history data and the selected test value; (f) automatically update the second requisition history data and compute a second simulated KPI value of the KPI by executing the target optimization model with the generated second demand data; (g) automatically store the computed, second simulated KPI value in association with the selected test value and the initialized calibration time value (h) automatically update the initialized calibration time value using the calibration incremental time; and (i) automatically repeat (e)-(h) until the updated, initialized calibration time value is greater than or equal to the calibration stop time.
To determine the best value for the calibrated parameter (service level or coefficient of variation), the computing device first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This process is repeated until the calibration time reaches its stop value.
16. The computing device of claim 15 , wherein after (i), selecting the calibrated parameter value of the first parameter further comprises computer-readable instructions that further cause the computing device to: (j) automatically compute a second aggregated KPI value as a sum of the stored second KPI values for each node at each value of the initialized calibration time value; (k) automatically update the selected test value using the incremental value; (l) automatically repeat (e)-(k) until the updated, selected test value is greater than or equal to the stop value; and automatically execute a stockpile parameter optimization model with the computed, second aggregated KPI values and the computed second aggregated KPI value; wherein the calibrated parameter value of the first parameter is selected based on results of execution of the stockpile parameter optimization model that ranks each value of the selected test value.
Building on the previous calibration process performed by a computing device, after simulating and storing KPI values for various test values of the parameter across a calibration time period, the device computes an aggregated KPI value for each test value. It then uses a stockpile parameter optimization model, which ranks each tested parameter value, to minimize backorders or maximize service levels, subject to a budget. The highest-ranked test value is then selected as the final calibrated parameter.
17. The computing device of claim 15 , wherein the computer-readable instructions further cause the computing device to automatically output the selected calibrated parameter value.
The computing device as described in the calibration process, where To determine the best value for the calibrated parameter (service level or coefficient of variation), the computing device first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This process is repeated until the calibration time reaches its stop value. Finally, the computing device outputs the selected, optimized calibrated parameter value.
18. The computing device of claim 15 , wherein the computer-readable instructions further cause the computing device to automatically output a comparison between the computed, second aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the calibration horizon time period.
The computing device as described in the calibration process, where To determine the best value for the calibrated parameter (service level or coefficient of variation), the computing device first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This process is repeated until the calibration time reaches its stop value. The device outputs a comparison between the simulated, aggregated KPI values and historical KPI data to show how well the selected parameters perform.
19. The computing device of claim 16 , wherein the stockpile parameter optimization model minimizes a total network backorder while satisfying a budget constraint defined by data read from a stockpile budget dataset.
In the stockpile parameter optimization model within the computing device, used to select the calibrated parameter, as described previously, the goal is to minimize the total number of backorders across the entire network while staying within a pre-defined budget read from a stockpile budget dataset.
20. The computing device of claim 16 , wherein the stockpile parameter optimization model maximizes a total network service level while satisfying a budget constraint defined by data read from a stockpile budget dataset.
In the stockpile parameter optimization model within the computing device, used to select the calibrated parameter, as described previously, the goal is to maximize the overall service level across the entire network while staying within a pre-defined budget read from a stockpile budget dataset.
21. A method of optimizing a stockpile a stockpile of an item comprising: automatically selecting, by a computing device, a calibrated parameter value of a first parameter; receive receiving an indicator of a validation horizon time period, wherein the validation horizon time period includes a start time, a stop time, and an incremental time; automatically initializing, by the computing device, a validation time value based on the start time; automatically reading, by the computing device, requisition history data from a requisition history dataset, wherein the requisition history data includes previous requisitions placed during a time period prior to the start time for an item in a network that includes a plurality of nodes; (a) automatically generating, by the computing device, demand data for each node of the plurality of nodes using a forecast model with the requisition history data and the selected calibrated parameter value, wherein the forecast model is configured to forecast a demand associated with the item at each node of the plurality of nodes; (b) automatically updating, by the computing device, the requisition history data and compute a simulated key performance indicator (KPI) value of a KPI by executing a target optimization model with the generated demand data, wherein the target optimization model is configured to determine a number and a time a new requisition is placed for the item at each node of the plurality of nodes; (c) automatically storing, by the computing device, the computed, simulated KPI value in association with the selected initial validation time value; (d) automatically updating, the initialized validation time value using the incremental time; automatically repeating, by the computing device, (a)-(d) until the updated, initialized validation time value is greater than or equal to the stop time; automatically computing, by the computing device, an aggregated KPI value as a sum of the stored KPI values for each node at each value of the initialized validation time value; outputting, by the computing device, a comparison between the computed, aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the validation horizon time period; and automatically optimizing, by the computing device, a stockpile of the item in the network for each node of the plurality of nodes using the selected calibrated parameter value and the computed aggregated KPI value.
A method optimizes item stockpiles across a network of locations. It selects a calibrated parameter (service level or coefficient of variation), simulates item demand at each location using historical data and a forecasting model incorporating the parameter. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored, aggregated, and compared to historical KPIs. The method adjusts the item stockpile at each location based on the calibrated parameter to improve performance. The process repeats over a validation time period (start, stop, increment).
22. The method of claim 21 , wherein the KPI is at least one of an on-hand disbursement value, a backlog value, a ready rate value, a fill rate value, and a back order ratio value.
The method described previously, where a method optimizes item stockpiles across a network of locations. It selects a calibrated parameter (service level or coefficient of variation), simulates item demand at each location using historical data and a forecasting model incorporating the parameter. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored, aggregated, and compared to historical KPIs. The method adjusts the item stockpile at each location based on the calibrated parameter to improve performance. The process repeats over a validation time period (start, stop, increment). The key performance indicator (KPI) used in the simulation and optimization process is selected from on-hand disbursement, backlog, ready rate, fill rate, or backorder ratio.
23. The method of claim 21 , wherein the first parameter is at least one of a service level and a coefficient of variation.
The method described previously, where a method optimizes item stockpiles across a network of locations. It selects a calibrated parameter (service level or coefficient of variation), simulates item demand at each location using historical data and a forecasting model incorporating the parameter. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored, aggregated, and compared to historical KPIs. The method adjusts the item stockpile at each location based on the calibrated parameter to improve performance. The process repeats over a validation time period (start, stop, increment). The calibrated parameter adjusted by the method can be either service level or coefficient of variation.
24. The method of claim 21 , further comprising automatically outputting, by the computing device, a comparison between the computed, aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the validation horizon time period to verify that the selected calibrated parameter value is configured correctly.
The method described previously, where a method optimizes item stockpiles across a network of locations. It selects a calibrated parameter (service level or coefficient of variation), simulates item demand at each location using historical data and a forecasting model incorporating the parameter. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored, aggregated, and compared to historical KPIs. The method adjusts the item stockpile at each location based on the calibrated parameter to improve performance. The process repeats over a validation time period (start, stop, increment). The method outputs a comparison between simulated and actual historical KPI data to verify the calibrated parameter is correctly configured.
25. The method of claim 21 , wherein selecting the calibrated parameter value of the first parameter comprises: receiving indicators of a start value, a stop value, and an incremental value of the first parameter; receiving an indicator of a calibration horizon time period, wherein the calibration horizon time period includes a calibration start time, a calibration stop time, and a calibration incremental time; automatically initializing, by the computing device, a calibration time value based on the calibration start time; automatically selecting, by the computing device, a test value for the first parameter as the start value; automatically reading, by the computing device, second requisition history data from the requisition history dataset, wherein the second requisition history data includes previous requisitions placed during the time period prior to the calibration start time for the item in the network that includes the plurality of nodes; (e) automatically generating, by the computing device, second demand data for each node of the plurality of nodes using the forecast model with the second requisition history data and the selected test value; (f) automatically updating, by the computing device, the second requisition history data and compute a second simulated KPI value of the KPI by executing the target optimization model with the generated second demand data; (g) automatically storing, by the computing device, the computed, second simulated KPI value in association with the selected test value and the initialized calibration time value (h) automatically updating, by the computing device, the initialized calibration time value using the calibration incremental time; and (i) automatically repeating, by the computing device, (e)-(h) until the updated, initialized calibration time value is greater than or equal to the calibration stop time.
To determine the best value for the calibrated parameter (service level or coefficient of variation), the method first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This process is repeated until the calibration time reaches its stop value.
26. The method of claim 25 , wherein after (i), selecting the calibrated parameter value of the first parameter further comprises: (j) automatically computing, by the computing device, a second aggregated KPI value as a sum of the stored second KPI values for each node at each value of the initialized calibration time value; (k) automatically updating, by the computing device, the selected test value using the incremental value; (l) automatically repeating, by the computing device, (e)-(k) until the updated, selected test value is greater than or equal to the stop value; and automatically executing, by the computing device, a stockpile parameter optimization model with the computed, second aggregated KPI values and the computed second aggregated KPI value; wherein the calibrated parameter value of the first parameter is selected based on results of execution of the stockpile parameter optimization model that ranks each value of the selected test value.
Building on the previous calibration method, after simulating and storing KPI values for various test values of the parameter across a calibration time period, the method computes an aggregated KPI value for each test value. It then uses a stockpile parameter optimization model, which ranks each tested parameter value, to minimize backorders or maximize service levels, subject to a budget. The highest-ranked test value is then selected as the final calibrated parameter.
27. The method of claim 25 , further comprising automatically outputting, by the computing device, the selected calibrated parameter value.
The method as described in the calibration process, where To determine the best value for the calibrated parameter (service level or coefficient of variation), the method first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This process is repeated until the calibration time reaches its stop value. Finally, the method outputs the selected, optimized calibrated parameter value.
28. The method of claim 25 , further comprising automatically outputting, by the computing device, a comparison between the computed, second aggregated KPI values and historical KPI data computed from an actual requisition history for each node of the plurality of nodes during the calibration horizon time period.
The method as described in the calibration process, where To determine the best value for the calibrated parameter (service level or coefficient of variation), the method first defines a calibration time period (start, stop, increment). It iterates through a range of test values for the parameter (start, stop, increment). For each test value, it simulates item demand at each location using historical data and a forecasting model. A target optimization model determines requisition timing, generating simulated KPI values. These KPIs are stored against the test value and the current calibration time. This process is repeated until the calibration time reaches its stop value. The method outputs a comparison between the simulated, aggregated KPI values and historical KPI data to show how well the selected parameters perform.
29. The method of claim 26 , wherein the stockpile parameter optimization model minimizes a total network backorder while satisfying a budget constraint defined by data read from a stockpile budget dataset.
In the stockpile parameter optimization model within the method, used to select the calibrated parameter, as described previously, the goal is to minimize the total number of backorders across the entire network while staying within a pre-defined budget read from a stockpile budget dataset.
30. The method of claim 26 , wherein the stockpile parameter optimization model maximizes a total network service level while satisfying a budget constraint defined by data read from a stockpile budget dataset.
In the stockpile parameter optimization model within the method, used to select the calibrated parameter, as described previously, the goal is to maximize the overall service level across the entire network while staying within a pre-defined budget read from a stockpile budget dataset.
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October 26, 2016
July 11, 2017
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